Integrating multiple document features in language models for expert finding

Zhu, Jianhan; Huang, Jimmy Xiangji; Song, Dawei; Rüger, Stefan

doi:10.1007/s10115-009-0202-6

Cited by 29 publications

(12 citation statements)

References 37 publications

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“…Here the external document structure features are the boolean variables to represent whether a document (in W3C) comes from specific types of documents (e.g., 8 = 1 means the document is either from "www" or "esw"). The evaluations on W3C use all the features, while the features 6 − 9 and 6 − 9 are not applied to CERC, as the CERC dataset does not contain explicit document types nor many emails with internal structure information useful for expert search [38]. The 1 feature is the document retrieval score by LM using the topic as the query.…”

Section: Methodsmentioning

confidence: 99%

“…These features include document authority information such as the PageRank, indegree, and URL length [38], graph-based expert authority [10], internal document structures that indicate the experts' associations with the content of documents [6], non-local evidence [2], and the evidence that can be acquired outside of an enterprise [29]. Additional evidence can be integrated by identifying home pages of candidate experts and clustering relevant documents [20].…”

Section: Related Workmentioning

confidence: 99%

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Discriminative models of integrating document evidence and document-candidate associations for expert search

Fang

Mathur

2010

Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Generative models such as statistical language modeling have been widely studied in the task of expert search to model the relationship between experts and their expertise indicated in supporting documents. On the other hand, discriminative models have received little attention in expert search research, although they have been shown to outperform generative models in many other information retrieval and machine learning applications. In this paper, we propose a principled relevance-based discriminative learning framework for expert search and derive specific discriminative models from the framework. Compared with the state-ofthe-art language models for expert search, the proposed research can naturally integrate various document evidence and document-candidate associations into a single model without extra modeling assumptions or effort. An extensive set of experiments have been conducted on two TREC Enterprise track corpora (i.e., W3C and CERC) to demonstrate the effectiveness and robustness of the proposed framework.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Discriminative models of integrating document evidence and document-candidate associations for expert search

Fang

Mathur

2010

Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…However, valuable information can be identified from documents generated within an organisation [8]. Automating expert finding from such documents provides an efficient and sustainable approach to expertise discovery.…”

Section: The Ou Expert Searchmentioning

confidence: 99%

The OU Linked Open Data: Production and Consumption

Zablith

Fernández

Rowe

2012

Lecture Notes in Computer Science

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Abstract. The aim of this paper is to introduce the current efforts toward the release and exploitation of The Open University's (OU) Linked Open Data (LOD). We introduce the work that has been done within the LUCERO project in order to select, extract and structure subsets of information contained within the OU data sources and migrate and expose this information as part of the LOD cloud. To show the potential of such exposure we also introduce three different prototypes that exploit this new educational resource: (1) the OU expert search system, a tool focused on finding the best experts for a certain topic within the OU staff; (2) the Buddy Study system, a tool that relies on Facebook information to identify common interest among friends and recommend potential courses within the OU that 'buddies' can study together, and; (3) Linked OpenLearn, an application that enables exploring linked courses, Podcasts and tags to OpenLearn units. Its aim is to enhance the browsing experience for students, by detecting relevant educational resources on the fly while reading an OpenLearn unit.

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“…For example, instead of capturing the associations at the document level, they may be estimated at the paragraph or snippet level [7,20,24]. Other extensions incorporate additional forms of evidence through the use of priors [14], document structure [33], hierarchical, organizational, and topical context and structure [6,23], and Web data [25].…”

Section: Related Workmentioning

confidence: 99%

A User-Oriented Model for Expert Finding

Смирнова

Balog

2011

Lecture Notes in Computer Science

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Abstract. Expert finding addresses the problem of retrieving a ranked list of people who are knowledgeable on a given topic. Several models have been proposed to solve this task, but so far these have focused solely on returning the most knowledgeable people as experts on a particular topic. In this paper we argue that in a real-world organizational setting the notion of the "best expert" also depends on the individual user and her needs. We propose a user-oriented approach that balances two factors that influence the user's choice: time to contact an expert, and the knowledge value gained after. We use the distance between the user and an expert in a social network to estimate contact time, and consider various social graphs, based on organizational hierarchy, geographical location, and collaboration, as well as the combination of these. Using a realistic test set, created from interactions of employees with a university-wide expert search engine, we demonstrate substantial improvements over a state-of-the-art baseline on all retrieval measures.

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Integrating multiple document features in language models for expert finding

Abstract: Integrating multiple document features in language models for expert finding. Knowledge and Information Systems, 23(1) pp. 29-54. For guidance on citations see FAQs.

Cited by 29 publications

References 37 publications

Discriminative models of integrating document evidence and document-candidate associations for expert search

Discriminative models of integrating document evidence and document-candidate associations for expert search

The OU Linked Open Data: Production and Consumption

A User-Oriented Model for Expert Finding

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